· Integrated metabolic and transcriptomic profiles reveal the germination-associated dynamic changes for Cassiae Semen

doi:10.21203/rs.3.rs-2126956/v1

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· Integrated metabolic and transcriptomic profiles reveal the germination-associated dynamic changes for Cassiae Semen

https://doi.org/10.21203/rs.3.rs-2126956/v1

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Introduction The seeds of Cassia obtusifolia L. (Cassiae Semen) have been widely used as both food and traditional Chinese medicine in China.

Objectives For better understanding the metabolic mechanism along with germination, different samples of Cassiae Semen at various germinating stages were collected.

Methods These samples were subjected to ¹H-NMR and UHPLC/Q-Orbitrap-MS based untargeted metabolomics analysis together with transcription analysis.

Results A total of fifty differential metabolites (mainly amino acids and sugars) and twenty key genes involved in multiple pathways were identified in two comparisons of different groups (36 h vs 12 h and 84 h vs 36 h). The metabolic and gene network for seed germination was depicted. In the germination of C. Semen, the fructose and mannose metabolism pathway was activated, indicating energy was more needed in the testa rupture period (36 h). In the embryonic axis elongation period (84 h), the pentose and glucuronate interconversions pathway, and phenylpropanoid biosynthesis pathway were activated, which suggested some nutrient sources (nitrogen and sugar) would be demanded. Furthermore, oxygen, energy and nutrition should be supplied through the whole germination process. These global views open up an integrated perspective for understanding the complex biological regulatory mechanism during seed germination process of C. Semen.

Cassia obtusifolia L.

Cassiae Semen

Seed germination

Metabolomics

Transcriptomics

Multivariate statistical analysis

Natural resources, which were produced from plants, have potential applications from food additive to pharmaceutical industries. Cassiae Semen (Leguminosae), which is the seeds of the two annual plant, including Cassia obtusifolia L. and Cassia tora L., is known as Juemingzi in traditional Chinese medicine (TCM). In 2002, Cassiae Semen was listed as one of the items that can be used as both food and drug by the Ministry of Health in China (Tang et al., 2015). Cassia is one of the largest genus of dicotyledonous plants, which is widely cultivated as a globally plant and dominantly consumed in drugs and health care products. C. Semen was used to improve eyesight and relieve constipation (Chinese Pharmacopoeia Commission Pharmacopoeia of the People’s Republic of China, 2020). Modern studies have widely investigated the phytochemistry (Luo et al., 2015), pharmacodynamics (Tang et al., 2015), pharmacokinetics (Guo et al., 2017) and hepatotoxicity (Xu et al., 2019; Yang et al., 2019) of C. Semen. The bioactive compounds of C. Semen, such as anthraquinones and naphthopyrones, play important roles in anti-hypertension, anti-hyperlipidaemia, anti-hypercholesterolaemia hepatoprotection, bacteriostasis, antioxidation and lose weight by regulating blood lipids (Patil et al., 2004). C. Semen has also been applied in many fields such as tea, natural pigments, and food grade colorants (Rogers et al., 2009). These medicinal qualities of C. Semen make it rapidly increase in demand of the market. Thus, it is important to focus on the seed germination of C. Semen in order to improve its quality and yield.

Seed germination plays a considerable role in ecologies and agronomics, especially in plant reproductive development, crop yield, and medicinal resource preservation. However, the presence of water-impermeable layer in the seed coat blocks off water uptake. Under favorable conditions, seeds can efficiently exit physical dormancy and proceed to germination (Ailton et al., 2020). Normally, soaking of seeds is prerequisite for germination, mainly softening the texture of the seeds and increasing oxygen penetration. Seed germination generally undergoes imbibition and terminates with breakthrough of the radicle from testa (Enrico et al., 2019), involving a lot of physiological-morphogenetic changes. This process is significantly affected by both genes and metabolites.

During sprouting, the polysaccharides and proteins macromolecules stored in the seeds are broken down into soluble carbohydrates and free amino acids to provide nutritive and synthesize components for the early stages of energy sources (Hiroyuki et al., 2010). Although the mechanisms of seed development are highly conserved in the evolution of flowering plants, results observed in a crop are not necessarily valid for another crop. However, much less is known about C. Semen, which may limit the understanding of the molecular mechanism of metabolic regulation across developmental stages during the seed-to-seedling transition. Numerous studies, performing on legumes model plants, have showed that germination of edible seeds involved de novo synthesis of bioactive compounds and enhances antioxidant capability (Gan et al., 2017; Yang et al., 2019; Do et al., 2016). It is also an effective way to improve the nutritional values. With the genome of Cassia tora L. has been released, physiological study performed on Cassia obtusifolia L., a non-model plant, has available genome information. In view of this, a comprehensive study covering the entire development of Cassiae Semen is imperative. For dissecting the mechanisms of action at the molecular level and expanding the understanding and application of germination, it is useful to improve the quality and application scope of medicinal plants.

Present studies build a bridge between the phenotype and genotype, highlighting an omics technique combined with multivariate data analysis techniques. Time-course metabolomics based ¹H-NMR and UHPLC-MS at downstream product accumulation, combined with based high-throughput next-generation sequencing transcriptomics at the upstream gene expression level, were applied in this study. This can add one more dimension to explore the perturbations of metabolic pathways, and obtain a comprehensive map of metabolic changes under seed germination (Yun et al., 2018). This global view of the transcriptional and metabolic changes could help to understand the complex regulatory mechanism during the seed-to-seedling transition in C. Semen.

2.1 Chemicals and Materials

Deionized water was purified by the Milli-Q system (Millipore, Bedford, MA, USA). Ultra-pure water was bought from Watsons Food & Beverage Co., Ltd. (Guangzhou, China). MS-grade formic acid was obtained from Sigma-Aldrich Trading Co., Ltd. Acetonitrile and ethanol (HPLC grade) were provided by Thermo Fisher Scientific Co., Ltd. (Pittsburgh, PA, USA). Agilent 2100 RNA Nano 6000 Assay Kit was obtained from Agilent Technologies Co., Ltd. Illumina TruseqTM RNA sample prep Kit.

Deuteroxide (D₂O, 99.9% D) and sodium 3-trimethylsilyl [2, 2, 3, 3-D4] propionate (TSP, δ_H = 0.00 ppm) were obtained from Cambridge Isotope Laboratory, Inc. (Tewksbury, MA, USA). Phosphate buffer (PB, 0.1M) was prepared by dissolving potassium phosphate dibasic anhydrous (K₂HPO₄) and sodium dihydrogen phosphate anhydrous (NaH₂PO₄, analytical grade, Macklin Biochemical Technology Co., Ltd. Shanghai, China) in D₂O with an appropriate molar ratio (4:1).

Cassiaside, rubrofusarin gentiobioside, rubrofusarin triglucoside, emodin, chrysophanol, obtusin, aurantio-obtusin, and chrysoobtusin were bought from National Institutes for Food and Drug Control (Beijing, China). Sucrose, raffinose, stachyose, arginine, histidine, asparagine, aspartic acid, threonine, glutamine, glutamic acid, pyroglutamic acid, malic acid, isoleucine, leucine, phenylalanine, and tryptophan were purchased from Shanghai Yuanye Bio-Technology Co., Ltd (Shanghai, China).

2.2 Plant Materials and Treatment Conditions

Cassia obtusifolia L. seeds were obtained from Dali city (Yunnan, P.R. China), and authenticated by professor Lijuan Zhang from Tianjin University of Traditional Chinese Medicine. Before germination, the seeds were sterilized for the surface in 75% (v/v) ethanol-H₂O for 10 min, and thoroughly washed until a neutral pH was obtained. Thirty-five grams of seeds were soaked in deionized water with an optimum temperature of 30°C for 6 h and 12 h, respectively. Subsequently, about 500 seeds (about 17 g) were uniformly placed in 24 sterile petri dishes that contained two layers wet filter paper. Treatments were carried out as follows: water was supplemented to the dishes every 4 h at room temperature (23°C), and six independent biological replicates (fifty similar phenotypes seeds per dish) were sampled at each time-point (6 h, 12 h, 22 h, 36 h, 60 h and 84 h) under identical controlled conditions. After wiped off surface moisture, fresh harvested seeds were quickly frozen and ground into fine powder with liquid N₂, then were transferred to a -80°C refrigerator until metabolic and transcriptomic analysis.

2.3 Metabolites Extraction and UHPLC-MS Analysis Based on Q Exactive^™ Orbitrap

Sample preparation and experimental condition on UHPLC-MS were listed in our previous study (Shi et al., 2021). The quality control (QC) sample was a mixture of all samples.

2.4 UHPLC-MS Data Processing and Analysis

All MS data obtained by Xcalibur (version 4.0) software (Thermo-Fisher Scientific, USA). A data matrix that consisted of the retention time (RT), mass-to-charge ratio (m/z) values, and ion intensity were extracted with Compound Discover 3.0 (Thermo-Fisher Scientific, USA). The data were aligned with mass window (< 0.05 mDa) and retention time window (< 0.2 min). The metabolic features were identified by using the accurate MS and MS² spectra with searching in reliable biochemical databases, such as HMDB, ChemSpider, Metlin, PubChem, MoNa database, literature references (Luo et al., 2015; Tang et al., 2015; Shi et al., 2021) and authentic standards.

2.5 Extraction and Metabolic Analysis Based on ¹H-NMR

Sample preparation and experimental condition on ¹H-NMR were also listed in our previous study (Shi et al., 2021). To quantify metabolites, peak integrals were obtained from ¹H-NMR spectra (or the deconvolution results) with MestReNova (version 9.0, Mestrelab Research, Spain).

2.6 NMR Data Processing and Analysis

The chemical shift of ¹H-NMR spectra was internally referenced to the TSP peak at δ_H 0.00 ppm. Spectral region of δ_H 4.7–4.9 ppm was discarded due to containing residual signals of water. The remaining data were normalized by range area, and the processed spectral regions at δ_H 0.80–9.60 ppm were bucketed into bins (0.003 ppm in width) by MestReNova.

2.7 Statistical Analysis for Data of LC-MS and NMR

Both LC-MS and NMR dataset were subjected to multivariate statistical analysis with SIMCA-P software (version 14.1, Umetrics, Umeå, Sweden). Differences among sample groups were visualized by principal component analysis (PCA). orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied for pairwise comparisons. Metabolites were defined as statistically significant when a variable importance in the projection (VIP) score calculating in OPLS-DA model was greater than 1.5 and single dimensional statistical analysis with paired Student’s t-test (p-values < 0.05) by SPSS (version 17.0, SPSS Inc., Chicago, IL, USA). Pathway analysis was further conducted by MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/) based on identified significantly different metabolites.

2.8 RNA-Seq Transcriptomic Analysis and Identification of Differentially Expressed Genes (DEGs)

2.8.1 RNA Isolation, cDNA Library Construction and Sequencing

A comparison of gene transcription at three time-points (12, 36 and 84 h) was carried out in triplicate. High-quality total RNA was isolated by RNA 6000 Nano Kit according to the manufacturer’s instructions. The concentration and the purity of RNA was measured using NanoDrop 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, United States) with RIN number > 7.0. The integrity of total RNA was assessed by 1% agarose gel electrophoresis (5 V/cm, 15 min). Then 1 µg of total RNA from each sample was used for cDNA library construction following the specifications of the Illumina Truseq^™ RNA sample prep Kit, which was then sequenced using Illumina Novaseq 6000 in Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China).

2.8.2 RNA-Sequencing Data Analysis

Raw reads obtained from the Illumina sequencing were further filtered to obtain high quality clean reads. Transcriptomic analysis was conducted on cleaned reads and mapped to the reference genome sequence (https://www.ncbi.nlm.nih.gov/genome/?term=Senna) by Hisat2 (http://ccb.jhu.edu/software/hisat2/index.shtml) to find the expression level of the transcripts. Quantification of gene expression levels was calculated as follows: fragments per kilobase of transcript per million fragments mapped (FPKM) using RSEM (Version 1.3.3, http://deweylab.biostat.wisc.edu/rsem/). Genes with an adjusted fold change (FC) ≥ 10 and p ≤ 0.05 found by DESeq2 (Version 1.24.0) were assigned as DEGs.

2.8.3 Sequence Assembly and Functional Gene Annotation

All assembled unigenes were aligned by BLAST search against the NCBI non-redundant protein (NR), SwissProt, Pfam, EggNOG, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, identified with the given unigene along with their functional annotations.

2.8.4 GO and KEGG Pathway Analysis of DEGs

GO enrichment analysis of DEGs was performed, and their function was described. Pathway significant enrichment analysis was carried out based on the KEGG pathway (https://www.kegg.jp).

3.1 Morphological Characteristics

C. Semen were typically indehiscent and separated by transversal septa, quite tough with strong testa structure, and warm water could increase the germination uniformity to an extent. In general, seed germination underwent imbibition and terminated when the radicle broke through the seed coat. The result was showed in Fig. 1, this process mainly underwent three visible stages. As the development of germination, no significant of shape change was observed in the first 6 h. During the fast imbibition stage (12 h), volume-enlarged seeds were regarded as potentially germinable and collected, and was only steady water uptake (22 h). The testa ruptures were visible in 36 h, due to the expansion of recognizable embryo and germinable seeds. It was followed by radicle protrusion (60 h) until the embryonic axis elongated as a young seedling (84 h).

3.2 Metabolomics Analysis Based on UHPLC-MS

Metabolomics profiles at six different growth stages were performed, 596 features were detected in the primary UHPLC-MS analysis, and further analysis suggested that only 87 features (Table S1) simultaneously matched the coupled tandem MS library (MS²). Metabolites identified by UHPLC-MS could be found in our previous study (Shi et al., 2021). The PCA score plot was adopted to monitor dynamic metabolic changes as well as possible inter-group differentiation. All sample spots (six biological replicates from each collection time-point) were plotted. Samples were classified into three groups clearly (Fig. 2). Samples from 6 h, 12 h and 22 h were collected closer, representing the metabolites were similar in phase-1, similar with the samples in phase-2 (36 h and 60 h), and phase-3 (84 h) samples were far away from the others.

In this case, different time points, including 12 h (the fast imbibition period), 36 h (the testa rupture period) and 84 h (the embryonic axis elongation period), were selected as representative periods of 3 phases, respectively. OPLS-DA model was applied to interpretation of differential metabolites among groups in 36 h vs 12 h and 84 h vs 36 h (Fig. 3).

In the S-plot, red dots represented the highest contribution features (VIP > 1.5 and p < 0.05). The structures of characteristic metabolites were elucidated by the comparison with online database, literatures and standard reference. As listed in Table 1, 28 metabolites were labelled to be the main contributors for the significant differences by comparing of the 36 h vs 12 h, and 35 metabolites were screened out for 84 h vs 36 h.

Table 1

Different metabolites and their content changes of C. Semen seed germination based on UHPLC-MS.
No.	Formula	Ion mode	R_t (min)	MS/MS (m/z)	Identification		84 vs 36 h group
1	C₆H₉N₃O₂	[M + H]⁺	0.63	156.07698, 110.07172, 83.06105	histidine*	↑	↑
2	C₆H₁₄N₄O₂	[M + H]⁺	0.61	175.11859, 158.09232, 130.09715, 116.07059, 70.06567, 60.05627	arginine*	↑	↑
3	C₄H₇NO₄	[M + H]⁺	0.63	116.03451, 88.03993, 74.02441	aspartic acid*	↑	-
4	C₄H₈N₂O₃	[M + H]⁺	0.63	116.03463, 88.03996, 87.05595, 74.02443	asparagine*	-	↑
5	C₅H₁₃NO	[M + H]⁺	0.63	104.10744, 87.04436, 60.08163	choline	-	↓
6	C₃H₇NO₃	[M + H]⁺	0.65	88.03931, 70.02874, 60.04439	serine*	↑	-
7	C₆H₁₂O₇	[M-H]⁻	0.66	195.05011, 129.01804, 75.00734, 177.03938, 99.00740, 87.00727	gluconic acid*	↓	↑
8	C₁₈H₃₂O₁₆	[M-H]⁻	0.64	161.04422, 119.03354, 113.02303, 101.02302, 89.02296, 71.01241, 59.01245	raffinose*	↓	↓
9	C₄H₉NO₃	[M + H]⁺	0.65	102.05552, 74.06082, 56.05040	threonine*	-	↑
10	C₅H₁₀N₂O₃	[M + H]⁺	0.66	130.05011, 102.05542, 84.04505, 56.05036	glutamine*	↑	-
11	C₆H₁₂O₆	[M-H]⁻	0.71	119.03318, 113.02320, 101.02293, 89.02294, 71.01241, 59.01244	mannose*	↑	↑
12	C₅H₇NO₃	[M + H]⁺	0.66	84.04, 56.04, 41.03	pyroglutamic acid*	↑	-
13	C₁₂H₂₂O₁₁	[M-H]⁻	0.68	179.05481, 161.04448, 143.03378, 119.03352, 113.02297, 101.02293, 89.02293, 71.01238, 59.01245	sucrose*	↑	-
14	C₅H₉NO₂	[M + H]⁺	0.66	116.07096, 70.06590	proline*	-	↑
15	C₂₄H₄₂O₂₁	[M-H]⁻	0.69	545.17255, 383.11966, 341.10873, 323.09863, 221.06596, 179.05501, 161.04428, 143.03355, 113.02302, 101.02301, 89.02296, 71.01241, 59.01248	stachyose*	↑	↓
16	C₄H₆O₅	[M-H]⁻	0.71	133.01294, 115.00230, 71.01249, 72.99168	malic acid*	-	↑
17	C₁₀H₁₇N₃O₆S	[M + H]⁺	0.74	291.06302, 233.05917, 179.04857, 162.02208, 142.03230, 142.03230, 130.05009, 116.01679, 84.04505, 76.02228, 58.99585	glutathione	-	↓
18	C₁₀H₁₃N₅O₄	[M + H]⁺	0.73	136.06195, 119.03548, 94.04022, 57.03422	adenosine*	↑	↑
19	C₅H₁₁NO₂S	[M + H]⁺	0.73	133.03195, 104.05332, 87.02694, 61.01149, 56.05037	methionine	↑	↑
20	C₆H₈O₇	[M-H]⁻	0.74	191.01871, 173.00798, 129.01787, 111.00738, 87.00735	citric acid*	↑	-
21	C₆H₁₃NO₂	[M + H]⁺	0.80	86.09709, 69.07070, 55.05505	leucine*	-	↑
22	C₉H₁₁NO₃	[M + H]⁺	0.97	165.05434, 147.04379, 136.07547, 123.04401, 119.04913, 95.04943, 91.05454	tyrosine	-	↑
23	C₁₀H₁₃N₅O₄	[M + H]⁺	1.04	136.06194, 119.03479	isomer of adenosine	↑	-
24	C₆H₁₃NO₂	[M + H]⁺	1.04	86.09710, 69.07069	isoleucine*	↑	↑
25	C₉H₁₁NO₂	[M + H]⁺	1.84	120.08113, 103.05470, 84.96029	phenylalanine*	-	↑
26	C₁₁H₁₂N₂O₂	[M + H]⁺	3.84	188.07079, 146.06021, 118.06550, 170.06018, 159.09186, 132.08104	tryptophan	-	↑
27	C₁₁H₉NO₂	[M + H]⁺	3.84	142.06508, 170.05951, 115.05448	3-indoleacrylic acid	↑	↑
28	C₃₈H₅₄O₂₄	[M-H]⁻	5.63	893.29324, 245.08134	norrubrofusarin-6-O-β-D-gentiobioside	↓	-
29	C₃₈H₅₄O₂₄	[M-H]⁻	5.63	893.29324, 245.08134	torachrysone-8-O-β-D-glucopyransyl-(1–6)-β-D-glucopyransyl-(1–3)-β-D-glucopyransyl-(1–6)-β-D-glucopyranoside	↓	-
30	C₂₇H₃₂O₁₅	[M + H]⁺	5.93	273.07593, 315.08505, 297.07578	rubrofusarin gentiobioside	↓	-
31	C₂₀H₂₀O₁₀	[M-H]⁻	5.99	257.04520, 215.03410, 419.09738	norrubrofusarin-6-O-β-D-glucopyranoside	↓	↓
32	C₁₄H₁₀O₅	[M + H]⁺	6.00	259.06036, 241.04964, 213.05492, 185.05968	alternariol	↓	↓
33	C₂₆H₃₄O₁₄	[M + H]⁺	6.07	247.09692, 325.10730	torachrysone-8-O-β-D-gentiobioside	↓	-
34	C₂₇H₃₂O₁₅	[M + H]⁺	6.28	273.07593, 297.07578, 315.08505	cassiaside C	↓	-
35	C₂₆H₃₀O₁₄	[M + H]⁺	6.53	273.07587, 315.08447	cassiaside B	↓	-
36	C₁₅H₁₀O₇	[M-H]⁻	7.22	283.0253, 257.0470, 178.9976, 151.0026, 121.0281, 107.0123, 83.01244, 61.9870	quercetin*	↑	-
37	C₁₅H₁₀O₇	[M-H]⁻	7.88	301.03482, 227.03392, 217.04947, 255.02913, 201.05505	isomer of quercetin	-	↓
38	C₁₆H₁₂O₆	[M-H]⁻	9.25	284.03226, 256.03723	hispidulin	-	↓
39	C₁₅H₁₀O₆	[M-H]⁻	9.62	285.04010, 241.04999, 213.05489	7-hydroxyemodin/2-hydroxyemodin	-	↓
40	C₁₆H₁₆O₆	[M + H]⁺	9.81	287.09171, 269.08102, 259.09677, 241.08604	cassialactone	-	↓
41	C₁₆H₁₂O₇	[M + H]⁺	10.10	317.06589, 289.07092, 259.06042, 247.09682, 196.01707, 154.99042, 130.53358, 110.02048	isorhamnetin	-	↓
42	C₁₆H₁₂O₅	[M-H]⁻	10.36	283.06073, 240.04207	obtusifolin	-	↓
43	C₁₉H₁₈O₇	[M + H]⁺	10.54	359.11282, 326.07877, 298.08362, 283.06003, 255.06546	chrysoobtusin	↓	↓
44	C₁₈H₁₆O₇	[M-H]⁻	11.36	343.08179, 328.05835, 298.01147, 285.03998, 313.03513, 242.02139	1-desmethylchryso-obtusin	-	↓
45	C₁₈H₁₆O₇	[M + H]⁺	12.17	345.09729, 312.06308, 330.07312	obtusin	-	↓
46	C₁₄H₁₄O₄	[M-H]⁻	12.89	245.08138, 230.05777, 215.03421, 159.04408	torachrysone	-	↓
47	C₁₅H₁₀O₅	[M-H]⁻	12.96	269.04517, 241.04973, 225.05495	emodin	-	↓
48	C₁₇H₁₄O₇	[M-H]⁻	13.14	329.06628, 314.04285, 271.02444, 299.01932, 243.02933	1-desmethylobtusin	-	↓
49	C₁₅H₁₂O₅	[M-H]⁻	13.43	271.06082, 256.03717, 228.04189	rubrofusarin	↑	-
50	C₃₁H₂₄O₈	[M-H]⁻	17.39	254.0582	Isomer of emodin (10/10') physcion dianthrone glycoside	-	↓
1. Symbol * represented the results were supported by standard compounds.
2. Symbol ↑, ↓ and - represented the increase, decrease and no significant difference of contents, respectively.

Compared with 12 h, the levels of amino acids (histidine, arginine, aspartic acid, serine, glutamine, pyroglutamic acid, methionine, and isoleucine) together with sugars (mannose, sucrose, and stachyose) and citric acid were increased in 36 h (Table 1). However, the contents of quercetin and adenosine were increased while the contents of gluconic acid and raffinose were decreased in 36 h in comparison to 12 h (Fig. 3, Table 1).

Comparing with 36 h, the increased levels of amino acids (histidine, arginine, threonine, proline, leucine, isoleucine, and phenylalanine), mannose, malate and adenosine together with the decreased levels of raffinose, stachyose and glutathione were observed at 84 h (Fig. 3, Table 1).

3.3 Metabolomics Analysis Based on ¹H-NMR

To visually observe the results, OPLS-DA models were built to reveal the significant metabolic differences between 36 h vs 12 h (Fig. 4A, B) and 84 h vs 36 h (Fig. 4C, D).

The color code in loading plots (Fig. 4B, D) changed from blue to red was corresponding to Pearson correlation coefficient of the variables increased from 0 to 1, indicating the weights of the discriminatory variables. Metabolites identified by NMR was presented in our previous study (Shi et al., 2021). Figure 4B showed that the upper section represented higher metabolites in 36 h, and the lower section denoted lower metabolites of 36 h compared with 12 h, same as is shown for 84 h vs 36 h in Fig. 4D.

Figure 4B showed that the levels of γ-aminobutyric acid (GABA) and isoleucine were increased while raffinose level was decreased in 36 h, compared with 12 h. As shown in Fig. 4D, compared with 36 h, the levels of tyrosine, malate, succinate, pyruvic acid, lysine, alanine, valine, leucine, and isoleucine were increased while the levels of α-galactose, sucrose and choline were decreased in 84 h.

Taking T1 values into consideration, our results showed that T1 of all the observed metabolites was less than 2 s from the fully relaxed spectra. Since the total repetition time for the fully relaxed spectra (about 10 s) was longer than 5T1, the absolute concentration of metabolites can be quantified with the use of deconvolution methods. The X-axis of icon consisted of six blocks that represented six time points of seed development. The ratios of changes were calculated in the form of (Ci-Co)/Co, where Ci and Co indicates metabolite concentrations from 6 biological replicates at time-point i and 0 (the C. Semen without germination), respectively.

As shown in Fig. 5, saccharides, amino acids, and TCA cycle intermediates were accumulated at 36 h, and reached the highest levels at 60 h. Around 84 h, above metabolites were return to the original level.

3.4 Pathway Analysis

Based on the differentially metabolites filtered from LC-MS and ¹H-NMR, pathway analysis was shown in Fig. 6. MetPA was used for the metabolic pathway analysis. The bubble scale represented the number of compounds, and the depth of the bubble color represented the p-value (red, lower p-value; yellow, higher p-value). The x-axis represented the pathway impact, and y-axis represented the pathway enrichment. p < 0.05 were considered as statistically significant level. Result was obtained from pathway analysis with MetPA (Impact > 0.10). The significantly different metabolites were mainly involved in alanine, aspartate and glutamate metabolism, galactose metabolism, glyoxylate and dicarboxylate metabolism, TCA cycle, glycine, serine and threonine metabolism, starch and sucrose metabolism, and other energy metabolism processes (Fig. 6).

3.5 Transcriptomic Analysis

A total of 45,268 unigenes were generated. The Q30 value was higher than 94.79%, and sequencing error rate of each sample was all lower than 0.1%. After raw quality filtering, a total of 43.02 Gb of clean sequence data (34 to 41 million clean reads per sample) were generated from nine samples. The purity of samples showed satisfactory (RIN ≥ 8.0, OD260/280 ≥ 1.8, OD260/230 ≥ 1.0). Values of Q20 (%) and Q30 (%) were both higher than 90% and the GC content was around 45% of the theoretical value, indicating the good quality of data output. These libraries with Q30 > 94.79% were perfectly matched to the foxtail millet reference sequences from 94.80–96.24%.

Around 61.8 Gb clean reads (6.07 G clean bases) were generated for each sample. After strict quality inspection and data cleansing, about 45.09, 47.83 and 46.57 million clean reads were generated for 12, 36 and 84 h, respectively. Of these, 41.7, 44.7 and 43.5 million unique reads, and 1.1, 1.3 and 1.2 million multiple reads, respectively, were mapped (Table S2).

As is shown in Fig. 7A, 1056 differentially expressed genes (DEGs) were found in stages of 36 h vs 12 h. Among these genes, 720 genes were up-regulated (red dots), the rest 336 genes were down-regulated (green dots) in 36 h in comparison to 12 h. A total of 587 genes were identified as differentially expressed while 508 genes were up-regulated and 79 genes were down-regulated in 84 h compared with 36 h. It implied that the number of up-regulated genes was always greater than down-regulated genes with the extension of germination time. The fact of most genes showing up-regulated, could provide clues for the mechanism of C. Semen seed germination. The results revealed 52 genes that overlapped between 36 h vs 12 h and 84 h vs 36 h, and these genes were up-regulated. Corresponding to that, 2 overlapped genes were down-regulated between these two groups (Fig. 7B).

Figure 7 (C, D) indicated that the distribution of the number of DEGs in the GO term mainly enriched in biological process (BP), cellular component (CC), and molecular function (MF). A mass of DEGs were enriched in cellular processes and metabolic processes in the whole BP, mainly performing the functions of binding and catalytic activity and involving components such as cell part and membrane part.

Figure 7E showed that, comparing with 12 h, the DEGs were mainly related to fructose and mannose metabolism; photosynthesis - antenna proteins; photosynthesis; stilbenoid, diarylheptanoid and gingerol biosynthesis; phenylpropanoid biosynthesis; ubiquinone and other terpenoid-quinone biosynthesis; and carotenoid biosynthesis; plant hormone signal transduction in 36 h. The pathway of fructose and mannose metabolism was the most enrichment pathway by identified 12 relating unigenes. It suggested glucose metabolism and photosynthesis were dominant, and energy was supplied for early stage of seed germination. Figure 7F showed that, comparing with 36 h, the DEGs were enriched in pentose and glucuronate interconversions; phenylpropanoid biosynthesis; photosynthesis-antenna proteins; and cutin, suberine and wax biosynthesis in 84 h. In the radicle growth process (84 h), photosynthesis was inactivated. The utilization mode was transformed from fructose and mannose to pentose and glucuronic acid.

3.6 Metabolite-Gene Correlation Network Analysis

To analyze the combination of transcriptomics and metabolomics data, a potential metabolic network associated with seed germination is proposed (Fig. 8). The DEGs correlated with this network was shown in Table S3. As shown in Table S3, the absolute values of Log₂FC on DEGs were all greater than 1, representing the expression of these genes showed significant difference in the development of seed germination.

3.6.1 Analysis of Key Metabolites and Genes During the Testa Rupture Period of C. Semen (36 h)

Our results indicated that dramatic metabolic changes occurred during seed development. In the process of seed germination, the embryo and endosperm present an oxygen consumption tendency after water absorption. Because of the limitation of seed coats and the imperfection of cell structure, seeds enter an anoxic state. Soaking resulted in degradation of raffinose family oligosaccharides (RFOs) including raffinose and stachyose, whereas sucrose showed increased trends (Fig. 8). Similar result was reported in Cicer arietinum (Manu et al., 2016). Compared with the fast imbibition period (12 h), in the testa rupture period of C. Semen (36 h), fructose and mannose metabolism pathway was activated (Fig. 7E). As shown in Fig. 8, sucrose was increased in the testa rupture period. As carbon and energy sources, sucrose and some monosaccharides, such as glucose, fructose, and mannose, play an important role in seed germination. Previous studies revealed that sucrose could induce an enlarged morphology and constant growth as well as wet weight accumulation of seeds (Aurora et al., 2017). Sucrose could transform into glucose and fructose, and glucose level was decreased in the testa rupture period (Fig. 8). Furthermore, fructose could transform into mannose which was increased in the testa rupture period (Fig. 8). 1,4-β-Mannan could also transform into mannose by mannan endo-1,4-β-mannosidase (MAN) gene, and MAN gene showed up-regulated trend in this process (Table S3). Fructose could transform into fructose-6-P and fructose-1,6-bisphosphatase I (FBP) gene could induce the process of the interconversion between fructose-6-P and fructose-1,6-bis-P. Similar with MAN gene, FBP gene was also up-regulated in the testa rupture period of C. Semen (Table S3).

Also, fructose-1,6-bis-P and glycerate are metabolites belonging to Calvin cycle, and glyceraldehyde-3-phosphate dehydrogenase (GAPA) gene were up-regulated in the testa rupture period (Fig. 8). CO₂ could participate in both Calvin cycle and photosynthesis pathway. Plant photosystem contains two main components: light harvesting complex and light reaction center complex (Mark et al., 2011; Sari et al., 2015). The light reaction stage is carried out in the chloroplast. The light energy is captured by the light chlorophyll a/b binding protein (LHCA/LHCB), which transmits light energy to the photosystem protein (Psa/Psb), drives the electron transport chain, promotes the synthesis of chloroplast coenzyme chlorophyll and the improvement of light energy utilization rate, and provides necessary oxygen to alleviate the problem due to the dense seed tissue. The LHCA/LHCB genes and Psa/Psb genes which participated in photosynthesis and photosynthesis-antenna proteins respectively, were all up-regulated in the testa rupture period of C. Semen (Fig. 8). The activated photosynthesis may contribute a considerable amount of oxygen to the seed, which further fuels energy-generating biochemical pathways, such as glycolysis (Vered et al., 2010).

Phosphoenolpyruvate could transform into shikimate which participated in the phenylalanine, tyrosine and tryptophan biosynthesis pathway. As Fig. 8 showed, in the testa rupture period, the plant hormone signal transduction pathway was activated (Mohammad et al., 2014). Plant hormones are involved in the seed germination process. Several hormones, such as auxin, can break seed dormancy and promote germination (Xue et al., 2021). Tyrosine aminotransferase (TAT) gene was down-regulated which has indirect effect on the transformation of auxin. Previous studies revealed that auxin-responsive protein IAA (AUX/IAA), auxin responsive GH3 gene family (GH3) and SAUR family protein (SAUR) gene were the genes participated in cell enlargement (Liu, 2019). In the testa rupture period, indole-3-pyruvate monooxygenase (YUCCA) gene and auxin influx carrier (AUX1) gene showed up-regulated trend while GH3 gene and SAUR gene showed down-regulated trend. Also, histidine-containing phosphotransfer protein (AHP) gene which participated in the cell division process was up-regulated (Table S3). The basic role of phytohormone signaling is to promote cell elongation (Xu et al., 2020). Aromatic amino acids (such as phenylalanine) are the main precursor to induce the synthesis of plant hormones (Vered et al., 2010). In phenylpropanoid biosynthesis pathway, shikimate could transform into phenylalanine which was increased in the testa rupture period, by phenylalanine ammonia-lyase (PAL) gene. The phenylpropanoid pathway plays an important role in the response and regulation of biotic and abiotic stresses (Aurora et al., 2017). Phenylalanine could transform into p-hydroxy-pheny lignin, guaiacyl lignin, 5-hydroxy-guaiacy lignin and syringyl lignin by cinnamyl-alcohol dehydrogenase and peroxidase. Lignin can function as physical barrier to prevent microbial attack and give structural support (Aurora et al., 2017). In the testa rupture period, the phenylpropanoid biosynthesis pathway was activated, and the expression levels of PAL, cinnamyl-alcohol dehydrogenase and peroxidase genes were all up-regulated.

Pyruvate, transformed from phosphoenolpyruvate showed a decreased level in the testa rupture period of C. Semen. Pyruvate could transform into acetyle-CoA and amino acids, such as leucine, valine, and alanine. Acetyl-CoA could transform into citrate (increased in the testa rupture period) which was participated in TCA cycle (Gad et al., 2014). In the testa rupture period, TCA cycle was activated (Fig. 8). The intermediate metabolites of TCA cycle, such as isocitrate was increased in the testa rupture period while malate showed a decreased trend. The result was consisted with the accumulation trend when sprouts supported indirectly the rise in energy production during seed germination (Mark et al., 2019). Oxaloacetate is one of the metabolites of TCA cycle, and has indirect effect with aspartic acid which could transform into lysine, asparagine and homoserine. Isoleucine was transformed from homoserine.

Protein and amino acid metabolism is activated during seed germination (Liu et al., 2018). Protein is the material basis of all biological life activities, while amino acids are the major transport forms of nitrogen in plants (Mechthild, 2014). Seed storage proteins provide not only energy, but also amino acids for seed germination (Liu et al., 2018). Amino acids could also serve as energy donors through their catabolism in the TCA cycle (Vered et al., 2010). In the testa rupture period of C. Semen, the levels of amino acids such as leucine, valine, alanine, aspartic acid, asparagine, and isoleucine were all increased (Fig. 8). Meanwhile, fructose and mannose metabolism pathway has indirect effect on pentose and glucuronate interconversions pathway. In the testa rupture period, pectinesterase gene and polygalacturonase gene showed up-regulated which induced poly(1,4-α-D-galacturonate) and D-galacturonate, respectively (Fig. 8).

3.6.2 Analysis of Key Metabolites and Genes During the Embryonic Axis Elongation Period of C. Semen (84 h)

Compared with the testa rupture period (36 h), in the embryonic axis elongation period (84 h) of C. Semen, the metabolites (such as glucose, mannose, and pyruvate) involved in fructose and mannose metabolism pathway all showed an increased trend while sucrose level showed decreased (Fig. 8). In addition, the levels of most of TCA cycle intermediates (isocitrate, succinate, and malate) were in increased. The results indicated that the fructose and mannose metabolism and TCA cycle were both activated. These two pathways could provide necessary energy for seed germination, the results suggested that more energy were required for C. Semen in the embryonic axis elongation period than that in the testa rupture period.

Isocitrate could transform into succinate though α-ketoglutarate which could transform into glutamate though glutamate synthase. In the embryonic axis elongation period, GABA shunt was activated (Fig. 8). The results indicated that the acceleration of TCA cycle might activate GABA shunt. Previous studies revealed that in germinated broccoli mannose could enhance GABA biosynthesis and polyamine degradation (Xie et al., 2021). The similar results were observed in the embryonic axis elongation period of C. Semen. The content of mannose was increased, consistently the contents of the metabolites of GABA shunt, such as GABA, glutamine, proline, and arginine all showed an increased trend (Fig. 8). The increase of glutamine could stimulate and stabilize nitrogen metabolism in the germination process of C. Semen. In addition, nitrogen metabolism is one of the most important metabolic events during germination, and nitrogen is an important nutrient factor during germination (Xue et al., 2021). An increased levels for most of amino acids (such as leucine, valine, alanine, asparagine, aspartic acid, and isoleucine) participating in amino acid metabolism might provide energy for the formation of embryonic axis (Fig. 8).

In the plant hormone signal transduction pathway, the levels of tryptophan and indole-3-acetic acid (IAA) were both increased, and YUCCA, AUX/IAA and SAUR genes were all up-regulated (Fig. 8). Phenylalanine was also increased in the embryonic axis elongation period, and the genes correlating to the transformation of lignin such as cinnamyl-alcohol dehydrogenase and peroxidase genes were all up-regulated (Fig. 8).

In photosynthesis pathway and photosynthesis-antenna proteins pathway, the Psa/Psb and LHCA/LHCB genes were all up-regulated in the embryonic axis elongation period of C. Semen, respectively (Fig. 8). The results showed that oxygen was still required for the embryonic axis elongation period. The genes, such as pectinesterase, polygalacturonase and pectate lyase (Pel) which participated in pentose and glucuronate interconversions pathway, were all up-regulated in the embryonic axis elongation period, and these results also supported the view of the pentose and glucuronate interconversions pathway activated (Fig. 8).

This study focused on the germination-associated dynamic changes of Cassiae Semen through the combination of metabolomics and transcriptomics. With the comparison of metabolites and its relating genes from different periods of C. Semen seed germination, the state of pathways which correlating with these following periods might be indicated the changes of seed germination. In the testa rupture period of C. Semen (36 h), the fructose and mannose metabolism pathway, plant hormone signal transduction pathway and Calvin cycle were all activated, providing the most of energy of the process and changing the seed status from dormancy to germination. While the pentose and glucuronate interconversions pathway and GABA shunt were activated in the embryonic axis elongation period (84 h), which provided the important nutrient factors (nitrogen and sugar) for the formation of embryonic axis. Also, the photosynthesis pathway, photosynthesis-antenna proteins pathway, phenylpropanoid biosynthesis pathway and amino acids pathway were all activated in both testa rupture period and embryonic axis elongation period. The correlating genes with these pathways have been provided oxygen, energy and nutrition for the whole process of seed germination. In conclusion, this study could provide new insights into the changes in metabolism and transcription during the seed germination of C. Semen.

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ‡These authors contributed equally.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENT

This work was supported by the Important Drug Development Fund, Ministry of Science and Technology of China (2019ZX09201005-002-007), the National Natural Science Foundation of China (32071990) and Interdisciplinary Cultivation Project (S21S1101).

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No competing interests reported.

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· Integrated metabolic and transcriptomic profiles reveal the germination-associated dynamic changes for Cassiae Semen

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Chemicals and Materials

2.2 Plant Materials and Treatment Conditions

2.3 Metabolites Extraction and UHPLC-MS Analysis Based on Q Exactive^™ Orbitrap

2.4 UHPLC-MS Data Processing and Analysis

2.5 Extraction and Metabolic Analysis Based on ¹H-NMR

2.6 NMR Data Processing and Analysis

2.7 Statistical Analysis for Data of LC-MS and NMR

2.8 RNA-Seq Transcriptomic Analysis and Identification of Differentially Expressed Genes (DEGs)

2.8.1 RNA Isolation, cDNA Library Construction and Sequencing

2.8.2 RNA-Sequencing Data Analysis

2.8.3 Sequence Assembly and Functional Gene Annotation

2.8.4 GO and KEGG Pathway Analysis of DEGs

3. Results And Discussions

3.1 Morphological Characteristics

3.2 Metabolomics Analysis Based on UHPLC-MS

3.3 Metabolomics Analysis Based on ¹H-NMR

3.4 Pathway Analysis

3.5 Transcriptomic Analysis

3.6 Metabolite-Gene Correlation Network Analysis

3.6.1 Analysis of Key Metabolites and Genes During the Testa Rupture Period of C. Semen (36 h)

3.6.2 Analysis of Key Metabolites and Genes During the Embryonic Axis Elongation Period of C. Semen (84 h)

4. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

· Integrated metabolic and transcriptomic profiles reveal the germination-associated dynamic changes for Cassiae Semen

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Chemicals and Materials

2.2 Plant Materials and Treatment Conditions

2.3 Metabolites Extraction and UHPLC-MS Analysis Based on Q Exactive™ Orbitrap

2.4 UHPLC-MS Data Processing and Analysis

2.5 Extraction and Metabolic Analysis Based on 1H-NMR

2.6 NMR Data Processing and Analysis

2.7 Statistical Analysis for Data of LC-MS and NMR

2.8 RNA-Seq Transcriptomic Analysis and Identification of Differentially Expressed Genes (DEGs)

2.8.1 RNA Isolation, cDNA Library Construction and Sequencing

2.8.2 RNA-Sequencing Data Analysis

2.8.3 Sequence Assembly and Functional Gene Annotation

2.8.4 GO and KEGG Pathway Analysis of DEGs

3. Results And Discussions

3.1 Morphological Characteristics

3.2 Metabolomics Analysis Based on UHPLC-MS

3.3 Metabolomics Analysis Based on 1H-NMR

3.4 Pathway Analysis

3.5 Transcriptomic Analysis

3.6 Metabolite-Gene Correlation Network Analysis

3.6.1 Analysis of Key Metabolites and Genes During the Testa Rupture Period of C. Semen (36 h)

3.6.2 Analysis of Key Metabolites and Genes During the Embryonic Axis Elongation Period of C. Semen (84 h)

4. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

2.3 Metabolites Extraction and UHPLC-MS Analysis Based on Q Exactive^™ Orbitrap

2.5 Extraction and Metabolic Analysis Based on ¹H-NMR

3.3 Metabolomics Analysis Based on ¹H-NMR